To　overcome　the　inefficiency　of　incremental　learning　for　hyperspectral　remote　sensing　images,　we　propose　a　binary　detection　theory-sequential　minimal　optimization　(BDT-SMO)　nonclass-incremental　learning　algorithm　based　on　hull　vectors　and　Karush-Kuhn-Tucker　conditions　(called　HK-BDT-SMO).　This　method　can　improve　the　accuracy　and　efficiency　of　BDT-SMO　nonclass-incremental　learning　for　fused　hyperspectral　images.　But　HK-BDT-SMO　cannot　effectively　solve　class-incremental　learning　problems　(an　increase　in　the　number　of　classes　in　the　newly　added　sample　sets).　Therefore,　an　improved　version　of　HK-BDT-SMO　based　on　hypersphere　support　vector　machine　(called　HSP-BDT-SMO)　is　proposed.　HSP-BDT-SMO　can　substantially　improve　the　accuracy,　scalability,　and　stability　of　HK-BDT-SMO　class-incremental　learning.　Ultimately,　HK-BDT-SMO　and　HSP-BDT-SMO　are　applied　to　the　classification　of　land　uses　with　fused　hyperspectral　images,　and　the　classification　results　are　compared　with　other　incremental　learning　algorithms　to　verify　their　performance.　In　nonclass-incremental　learning,　the　accuracy　of　HSP-BDT-SMO　and　HK-BDT-SMO　is　approximately　the　same　and　is　higher　than　the　others,　and　the　former　has　the　best　learning　speed;　while　in　class-incremental　learning,　HSP-BDT-SMO　has　a　better　accuracy　and　more　continuous　stability　than　the　others　and　the　second　highest　learning　speed　next　to　HK-BDT-SMO.　Therefore,　HK-BDT-SMO　and　HSP-BDT-SMO　are　excellent　algorithms　which　are　respectively　suitable　to　nonclass　and　class-incremental　learning　for　fused　hyperspectral　images.　(C)　2015　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)